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Research and application of constructing a coastal erosion risk prediction model based on LSTM
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Dong Liang1, 2, Na Gao1, Xiaoming Ying1, 2, Zeng Zhou3, Xiejun Shu1, Wanming Xu1, Mingli Zhao1, 2, *
Haiyang Xuebao | 2024, 46(6) : 130 - 140
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Haiyang Xuebao | 2024, 46(6): 130-140
• Article •
Research and application of constructing a coastal erosion risk prediction model based on LSTM
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Dong Liang1, 2, Na Gao1, Xiaoming Ying1, 2, Zeng Zhou3, Xiejun Shu1, Wanming Xu1, Mingli Zhao1, 2, *
Affiliations
  • 1. South China Sea Development Research Institute, Ministry of Natural Resources, Guangzhou 510300, China
  • 2. Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510300, China
  • 3. Jiangsu Provincial Key Laboratory of Coastal Ocean Resources Development and Environment Security, Hohai University, Nanjing 210098, China
Published: 2024-06-30 doi: 10.12284/hyxb2024059
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Shoreline erosion prediction is one of the hot issues in coastal dynamic geomorphology research. Based on the long short term memory (LSTM), the data of shoreline, water depth, intertidal zone width , and wave and tidal current for ERA5 inversion clollected from 1985 to 2023 near Sheyang County of Jiangsu Province were used to construct a coastal erosion risk prediction model in this study. The prediction model could accurately predict the nonlinear/linear change trend of accelerated erosion, stable erosion or coastline sedimentation. The results showed that the increasing of wave and tidal currents was the main factor of coastal erosion in Sheyang area in recent 20 years under the condition of sand source reduction. Besides, an ideal experiment of coastal protection activities was conducted by using the prediction model, and the protection effects of coastal reinforcement, wave dissipation and weak current engineering were discussed. The results showed that the protection effect of coastal reinforcement is the best, and wave dissipation is better than weak current. The prediction model is reasonable, and has great application value and development potential.

coastal erosion prediction  /  Sheyang, Jiangsu  /  LSTM  /  Nonlinear variation
Dong Liang, Na Gao, Xiaoming Ying, Zeng Zhou, Xiejun Shu, Wanming Xu, Mingli Zhao. Research and application of constructing a coastal erosion risk prediction model based on LSTM[J]. Haiyang Xuebao, 2024 , 46 (6) : 130 -140 . DOI: 10.12284/hyxb2024059
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Year 2024 volume 46 Issue 6
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Article Info
doi: 10.12284/hyxb2024059
  • Receive Date：2024-01-24
  • Online Date：2025-11-26
  • Published：2024-06-30
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History
  • Received：2024-01-24
  • Revised：2024-05-13
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Affiliations
    1. South China Sea Development Research Institute, Ministry of Natural Resources, Guangzhou 510300, China
    2. Key Laboratory of Marine Environmental Survey Technology and Application, Ministry of Natural Resources, Guangzhou 510300, China
    3. Jiangsu Provincial Key Laboratory of Coastal Ocean Resources Development and Environment Security, Hohai University, Nanjing 210098, China
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